Updates: DST data

exploratory analysis progress of the DST data, master thesis Lotte Pohl, 16.2.2023

where we left off

6 out of 8 recovered tags stopped recording 13-38 days after tagging, i.e. the animal died.

map
info

tag_serial_number	release_date_time	detected	sex	life_stage	length1	length1_unit	weight	weight_unit	capture_depth	recapture_date_time	animal_id	tag_type	scientific_name	release_latitude	release_longitude
1293322	2018-07-19 09:00:00	no	f	unknown	80	cm	NA	NA	3-20m	2019-09-29	3175	acoustic-archival	Mustelus asterias	51.61002	3.649600
1293319	2018-07-19 12:00:00	yes	f	unknown	73	cm	NA	NA	3-20m	2018-08-21	3178	acoustic-archival	Mustelus asterias	51.61177	3.649833
1293295	2018-07-19 13:00:00	yes	m	adult	78	cm	NA	NA	3-20m	2018-09-27	3182	acoustic-archival	Mustelus asterias	51.61250	3.653783
1293304	2019-07-11 10:25:00	no	f	mature	72	cm	1.35	kg	NA	NA	5067	acoustic-archival	Mustelus asterias	51.61502	3.657802
1293310	2019-07-11 12:20:00	no	m	mature	72	cm	1.40	kg	NA	2019-09-03	5069	acoustic-archival	Mustelus asterias	51.61822	3.663183
1293312	2019-07-12 08:15:00	no	f	unknown	91	cm	2.80	kg	NA	2019-08-29	5218	acoustic-archival	Mustelus asterias	51.61058	3.656550

Looking at the 6 tracks in more detail, it strikes that 3 sharks swam to the wind farms while 3 sharks stayed very close to the release location, namely tag 304 (female, 13 days of liberty), tag 310 (male, 37 days of liberty) and tag 312 (females, 32 days of liberty). Those 3 sharks were released in 2019.

depth and temperature profiles

This shark (male) swam out to the OWF.

This shark (female) stayed in the Oosterscheldt.

This shark (male) stayed in the Oosterscheldt.

This shark (female) stayed in the Oosterscheldt.

This shark (female) swam out to the OWF.

depth and temp profiles of long term DST data

These are the profiles of the 2 tags that recorded for over a year.

tag 308
tag 321

This shark (female) swam south into the English Channel.

This shark (male) swam north into the Southern North Sea.

what I plan to do now

I would like to segment the vertical data logs into behavioural patterns. This was done by Heerah et al., 2017 (https://doi.org/10.1186/s40462-017-0111-3). I spent a day with it already, but it is not trivial to format the code to the M. asterias DST data. To correctly do this, I would need the help of someone that has already done this. Jolien supervised a thesis in 2020 (Camille Boileau-Locas), that did that as well, but I know she has a lot to do right now. Mathieu Woillez also co-authored the paper by Karine Heerah, so I could contact him as well, potentially. What do you suggest?
The method of Heerah et al., 2017 uses STFT, a modified version of FFT. I was thinking that I maybe at first do a regular FFT to look at the periodic patterns of the signals.
Griffiths et al., 2020 (https://doi.org/10.1371/journal.pone.0239480) did not use any fancy/complex Hidden Markov Models like Heerah but they calculated statistics such as the time spent at certain depth bins, vertical speed, seabed proximity, or vertical movement behvaiour (based on a Wilcoxon test).
I am unsure right now what analyses could best shed light on behavioural patterns of the sharks. What are your suggestions?

--- title: "Updates: DST data" execute: echo: false include: false output: html_document: df_print: paged theme: readable toc_float: true toc: true number_sections: false toc_depth: 2 subtitle: exploratory analysis progress of the DST data, master thesis Lotte Pohl, 16.2.2023 editor_options: chunk_output_type: console knitr: opts_chunk: collapse: true format: html: page-layout: article code-tools: true fig-width: 8 fig-height: 5 ---    ::: column-body-outset ```{r libraries} #| include: false library(tidyverse) library(lubridate) library(leaflet) library(tibble) library(stringr) library(ggsci) library(pander) library(mregions2) library(fuzzyjoin) library(sf) # library(oce) library(plotly) ``` ```{r knityes_no} knitting <- T # if the document is not knitting right now, the prefix for path is set to ".", otherwise (for knitting) it's set to ".." pre <- ifelse(knitting %>% isFALSE(), ".", "..") pre <- "C:/Users/lotte.pohl/Documents/github_repos/ADST_Mustelus_asterias" ``` ```{r load_functions} source(paste0(pre, "/functions/functions_DST_presstemplogs.R")) source(paste0(pre, "/functions.R")) ``` ```{r setup} #| include: false knitr::opts_chunk$set(warning = FALSE, message = FALSE) ``` ```{r geo_info} #| include: false BPNS <- mregions2::gaz_search(3293) %>% mregions2::gaz_geometry() # sandbanks <- mregions2::gaz_search(c(2419, 4675, 2421, 2420)) %>% mregions2::gaz_geometry() # for now, as long as mregions2 does not load in rstudio server # bpns_boundaries <- st_read('./spatial_data/bpns_boundaries/bpns_boundaries.shp', layer = 'bpns_boundaries') sandbanks <- st_read(paste0(pre, '/spatial_data/sea_floor_geomorphology/sea_floor_geomorphology.shp'), layer = 'sea_floor_geomorphology') OWFs <- st_read(paste0(pre,"/spatial_data/OWFs_shapefiles/RD20140320_art8_1_20140328.shp")) ``` ```{r map_base} #| include: false map_base <- create_basic_map(include_regions = T) %>% setView(3.5, 51.5, zoom = 10) %>% #setView(-3, 52.5, zoom = 9) %>% addScaleBar(position = "topleft", options = scaleBarOptions(maxWidth = 250, imperial = F)) %>% # add sandbanks addPolygons(data=sandbanks, fillColor = "#FFF2B3", weight = 0, fillOpacity = 0.5, group = "Sandbanks") %>% #label = ~preferredGazetteerName, labelOptions = labelOptions(noHide = F, textOnly = F, direction = "center", textsize = "12px", sticky = FALSE) # add OWFs addPolygons(data=OWFs, color = "red", opacity = 0.5, fillColor = "red", weight = 1, fillOpacity = 0.2, group = "OWFs") %>% # add BPNS boundary addPolygons(data = BPNS, color = "grey", weight = 1, opacity = 1.0, fillOpacity = 0, label = ~preferredGazetteerName, labelOptions = labelOptions(noHide = F, textOnly = F, direction = "center", textsize = "12px", sticky = FALSE), group = "Regions") %>% leafem::addMouseCoordinates() map_base # base_map %>% save_leaflet(path = "./visualisation_ADST/maps_plots", filename = "basic_map_test", filetype = "pdf") #do with other map ``` ```{r general_info} #| include: false project_code <- "ADST-Shark" scientific_name <- "Mustelus asterias" masterias_recaptured_serials <- c("1293295", "1293304", "1293310", "1293312", "1293319", "1293308", "1293321", "1293322") # bounding box min_lon <- 0.8 max_lon <- 5 max_lat <- 53 min_lat <- 50 ``` ```{r load_RData} masterias_info <- read_csv(paste0(pre, "/RData/masterias_info.csv"), show_col_types = FALSE) masterias_info <- masterias_info %>% remove_double_cols() # masterias_stations <- read_csv(paste0(pre, "/RData/masterias_stations.csv"), show_col_types = FALSE) masterias_ind_per_station <- read_csv(paste0(pre, "/RData/masterias_ind_per_station.csv"), show_col_types = FALSE) masterias_detections <- read_csv(paste0(pre, "/RData/masterias_detections.csv"), show_col_types = FALSE) masterias_deployments <- read_csv(paste0(pre, "/RData/masterias_deployments.csv"), show_col_types = FALSE) detections_per_station <- read_csv(paste0(pre, "/RData/detections_per_station.csv"), show_col_types = FALSE) detections_per_deployment <- read_csv(paste0(pre, "/RData/detections_per_deployment.csv"), show_col_types = FALSE) close_stations <- read_csv(paste0(pre, "/RData/close_stations.csv"), show_col_types = FALSE) deployments <- read_csv(paste0(pre, "/RData/deployments.csv"), show_col_types = FALSE) ``` ```{r clean_animal_data} masterias_info <- clean_animal_info(masterias_info, masterias_detections) # make dateyear and time column in masterias_detections masterias_detections$date <- masterias_detections$date_time %>% format("%F") masterias_detections$time <- masterias_detections$date_time %>% format("%H:%M") # remove tags with too few detections - Jan said NO! masterias_detections_clean <- masterias_detections %>% filter(!tag_serial_number %in% c("1293319", "1293317", "1293316", "1293314","1293311", "1293309", "1293301", "1293296", "1293295")) # remove deployments outside of detection time frame deployments <- deployments %>% filter(recover_date_time < masterias_detections_clean$date_time %>% max()) # %>% filter(deploy_date_time > masterias_detections_clean$date_time %>% min()) ``` ```{r read_DST} #| include: false # mpt_1293295_Det0 <- read_csv("../dst_data/01_modelled_tracks/1293295/Sat0_Catch1_Det0_Behav0_res1.0/D_mle/Tracks/MA_SN1293295_mpt_track_positions.csv", show_col_types = FALSE) mpt_1293295_Det1 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293295/Sat0_Catch1_Det1_Behav0_res1.0/D_mle/Tracks/MA_SN1293295_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293295") # press_1293295 <- read_csv("../dst_data/02_pressure_temperature/ADST_log_SN1293295/SN1293295_pressure_log.csv", show_col_types = FALSE) # temp_1293295 <- read_csv("../dst_data/02_pressure_temperature/ADST_log_SN1293295/SN1293295_temperature_log.csv", show_col_types = FALSE) mpt_1293304_Det0 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293304/Sat0_Catch1_Det0_Behav0_res1.0/D_mle/Tracks/MA_SN1293304_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293304") mpt_1293308_Det1 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293308/Sat0_Catch1_Det1_Behav0_res1.0/D_mle/Tracks/MA_SN1293308_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293308") mpt_1293310_Det0 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293310/Sat0_Catch1_Det0_Behav0_res1.0/D_mle/Tracks/MA_SN1293310_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293310") mpt_1293312_Det0 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293312/Sat0_Catch1_Det0_Behav0_res1.0/D_mle/Tracks/MA_SN1293312_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293312") # mpt_1293319_Det0 <- read_csv("../dst_data/01_modelled_tracks/1293319/Sat0_Catch1_Det0_Behav0_res1.0/D_mle/Tracks/MA_SN1293319_mpt_track_positions.csv"), show_col_types = FALSE) mpt_1293319_Det1 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293319/Sat0_Catch1_Det1_Behav0_res1.0/D_mle/Tracks/MA_SN1293319_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293319") mpt_1293321_Det1 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293321/Sat0_Catch1_Det1_Behav0_res1.0/D_mle/Tracks/MA_SN1293321_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293321") mpt_1293322_Det1 <- read_csv(paste0(pre, "/dst_data/01_modelled_tracks/1293322/Sat0_Catch1_Det1_Behav0_res1.0/D_mle/Tracks/MA_SN1293322_mpt_track_positions.csv"), show_col_types = FALSE) %>% mutate(tag_serial_number = "1293322") ``` ```{r wrangle_dst_data} #| include: false # make one df combining all DST trajectories mpts_all <- rbind(mpt_1293295_Det1, mpt_1293304_Det0, mpt_1293308_Det1, mpt_1293310_Det0, mpt_1293312_Det0, mpt_1293319_Det1, mpt_1293321_Det1, mpt_1293322_Det1) %>% rename(detection_latitude = lat_dg, detection_longitude = long_dg, date_time = date) %>% select(tag_serial_number, detection_latitude, detection_longitude, date_time) # change date format mpts_all$date <- mpts_all$date_time %>% format("%m-%d") # mpts_all$tag_serial_number <- mpts_all$tag_serial_number %>% as.factor() # mpts_all$tag_serial_number <- mpts_all$tag_serial_number %>% as.character() mpts_all <- mpts_all %>% left_join(masterias_info %>% select(tag_serial_number, sex, life_stage, length1, weight), by = "tag_serial_number") mpts_short <- mpts_all %>% filter(!tag_serial_number %in% c("1293308", "1293321")) # # change date to 2018 of 3 animals tagged 2019 # mpts_2019 <- rbind(mpt_1293304_Det0, mpt_1293310_Det0, mpt_1293312_Det0) # mpts_2019$date <- mpts_2019$date - years(1) # # mpts_short <- rbind(mpts_2019, mpt_1293295_Det1, mpt_1293319_Det1, mpt_1293322_Det1) %>% # rename(detection_latitude = lat_dg, detection_longitude = long_dg, date_time = date) %>% select(tag_serial_number, detection_latitude, detection_longitude, date_time) # remove single files rm(mpt_1293295_Det1, mpt_1293304_Det0, mpt_1293308_Det1, mpt_1293310_Det0, mpt_1293312_Det0, mpt_1293319_Det1, mpt_1293321_Det1, mpt_1293322_Det1) ```    ```{r info sharks} summary_masterias_length <- masterias_info %>% group_by(sex) %>% summarise(mean_length = length1 %>% mean(), sd_length = length1 %>% sd()) ```   ## where we left off 6 out of 8 recovered tags stopped recording 13-38 days after tagging, i.e. the animal died. ::: panel-tabset ### map ```{r shortterm_map} #| include: true # pal_dst_short <- colorNumeric(palette = "viridis", domain = mpts_short$date %>% as.cha) # legend_dates_short = c("2018-07-15", "2018-08-01", "2018-08-15") col_fun <- ggsci::pal_locuszoom()(mpts_short$tag_serial_number %>% unique() %>% length()) pal <- leaflet::colorFactor(col_fun, domain = mpts_short$tag_serial_number) map_dst_short <- map_base %>% # add tag 295 addPolylines(data = mpts_short %>% filter(tag_serial_number == "1293295"), lat = ~detection_latitude, lng = ~detection_longitude, color = ~pal(tag_serial_number), weight = 3, opacity = 0.6, label = ~tag_serial_number, group = "tag 295") %>% addCircleMarkers(data = mpts_short %>% filter(tag_serial_number == "1293295"), lat = ~detection_latitude, lng = ~detection_longitude, fillColor = ~pal(tag_serial_number), radius = 5, weight = 0, fillOpacity = 0.8, label = ~paste0(format(date_time,"%F"), ", ", sex), group = "tag 295") %>% # add tag 304 addPolylines(data = mpts_short %>% filter(tag_serial_number == "1293304"), lat = ~detection_latitude, lng = ~detection_longitude, color = ~pal(tag_serial_number), weight = 3, opacity = 0.6, label = ~tag_serial_number, group = "tag 304") %>% addCircleMarkers(data = mpts_short %>% filter(tag_serial_number == "1293304"), lat = ~detection_latitude, lng = ~detection_longitude, fillColor = ~pal(tag_serial_number), radius = 5, weight = 0, fillOpacity = 0.8, label = ~paste0(format(date_time,"%F"), ", ", sex), group = "tag 304") %>% # add tag 310 addPolylines(data = mpts_short %>% filter(tag_serial_number == "1293310"), lat = ~detection_latitude, lng = ~detection_longitude, color = ~pal(tag_serial_number), weight = 3, opacity = 0.6, label = ~tag_serial_number, group = "tag 310") %>% addCircleMarkers(data = mpts_short %>% filter(tag_serial_number == "1293310"), lat = ~detection_latitude, lng = ~detection_longitude, fillColor = ~pal(tag_serial_number), radius = 5, weight = 0, fillOpacity = 0.8, label = ~paste0(format(date_time,"%F"), ", ", sex), group = "tag 310") %>% # add tag 312 addPolylines(data = mpts_short %>% filter(tag_serial_number == "1293312"), lat = ~detection_latitude, lng = ~detection_longitude, color = ~pal(tag_serial_number), weight = 3, opacity = 0.6, label = ~tag_serial_number, group = "tag 312") %>% addCircleMarkers(data = mpts_short %>% filter(tag_serial_number == "1293312"), lat = ~detection_latitude, lng = ~detection_longitude, fillColor = ~pal(tag_serial_number), radius = 5, weight = 0, fillOpacity = 0.8, label = ~paste0(format(date_time,"%F"), ", ", sex), group = "tag 312") %>% # add tag 319 addPolylines(data = mpts_short %>% filter(tag_serial_number == "1293319"), lat = ~detection_latitude, lng = ~detection_longitude, color = ~pal(tag_serial_number), weight = 3, opacity = 0.6, label = ~tag_serial_number, group = "tag 319") %>% addCircleMarkers(data = mpts_short %>% filter(tag_serial_number == "1293319"), lat = ~detection_latitude, lng = ~detection_longitude, fillColor = ~pal(tag_serial_number), radius = 5, weight = 0, fillOpacity = 0.8, label = ~paste0(format(date_time,"%F"), ", ", sex), group = "tag 319") %>% # add tag 322 addPolylines(data = mpts_short %>% filter(tag_serial_number == "1293322"), lat = ~detection_latitude, lng = ~detection_longitude, color = ~pal(tag_serial_number), weight = 3, opacity = 0.6, label = ~tag_serial_number, group = "tag 322") %>% addCircleMarkers(data = mpts_short %>% filter(tag_serial_number == "1293322"), lat = ~detection_latitude, lng = ~detection_longitude, fillColor = ~pal(tag_serial_number), radius = 5, weight = 0, fillOpacity = 0.8, label = ~paste0(format(date_time,"%F"), ", ", sex), group = "tag 322") %>% addLayersControl(position = "topright", overlayGroups = c("tag 295", "tag 304", "tag 310", "tag 312", "tag 319", "tag 322"), options = layersControlOptions(collapsed = FALSE)) # %>% # addLegend(position = "topleft", # colors = legend_dates_short %>% as.POSIXct() %>% pal_dst_short(), # labels = legend_dates_short, opacity = 1, # title = "Dates") map_dst_short ``` ### info ```{r info_shorttermDST} #| include: true knitr::kable(masterias_info %>% filter(tag_serial_number %in% c("1293295", "1293304", "1293310", "1293312", "1293319", "1293322")) %>% select(animal_id, tag_serial_number, tag_type, scientific_name, capture_depth, release_date_time, release_latitude, release_longitude, recapture_date_time, length1, length1_unit, weight, weight_unit, sex, life_stage, detected) %>% relocate(tag_serial_number, release_date_time, detected, sex, life_stage, length1, length1_unit, weight, weight_unit, capture_depth, recapture_date_time)) ``` ::: Looking at the 6 tracks in more detail, it strikes that 3 sharks swam to the wind farms while 3 sharks stayed very close to the release location, namely tag 304 (female, `r mpts_short %>% filter(tag_serial_number == "1293304") %>% nrow()` days of liberty), tag 310 (male, `r mpts_short %>% filter(tag_serial_number == "1293310") %>% nrow()` days of liberty) and tag 312 (females, `r mpts_short %>% filter(tag_serial_number == "1293312") %>% nrow()` days of liberty). Those 3 sharks were released in 2019.   ```{r read_presslog} colnames <- c("ADST DATA LOG", "1.0.0", "AdstConverter-1.0.5", "...4", "...5", "...6", "...7", "...8", "...9", "...10", "...11") press_295 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293295/SN1293295_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_304 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293304/SN1293304_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_308 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293308/SN1293308_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_310 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293310/SN1293310_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_312 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293312/SN1293312_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_319 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293319/SN1293319_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_322 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293322/SN1293322_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) press_321 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293321/SN1293321_pressure_log.csv"), show_col_types = FALSE, col_names = colnames) ``` ```{r read_templog} temp_295 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293295/SN1293295_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_304 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293304/SN1293304_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_308 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293308/SN1293308_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_310 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293310/SN1293310_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_312 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293312/SN1293312_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_319 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293319/SN1293319_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_322 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293322/SN1293322_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) temp_321 <- read_csv(paste0(pre, "/dst_data/02_pressure_temperature/ADST_log_SN1293321/SN1293321_temperature_log.csv"), show_col_types = FALSE, col_names = colnames) ``` ```{r wrangle_temppress} press_temp_295 <- create_press_temp(press_295, temp_295, "1293295", cutoff_days = 10) press_temp_304 <- create_press_temp(press_304, temp_304, "1293304", cutoff_days = 10) press_temp_308 <- create_press_temp(press_308, temp_308, "1293308", cutoff_days = 10) press_temp_310 <- create_press_temp(press_310, temp_310, "1293310", cutoff_days = 10) press_temp_312 <- create_press_temp(press_312, temp_312, "1293312", cutoff_days = 10) press_temp_319 <- create_press_temp(press_319, temp_319, "1293319", cutoff_days = 10) press_temp_322 <- create_press_temp(press_322, temp_322, "1293322", cutoff_days = 10) press_temp_321 <- create_press_temp(press_321, temp_321, "1293321", cutoff_days = 10) # remove unneeded files # rm(temp_295, temp_304, temp_310, temp_312, temp_319, temp_322, # press_295, press_304, press_310, press_312, press_319, press_322) ``` ## depth and temperature profiles ::: panel-tabset ### tag 295 This shark (male) swam out to the OWF. ```{r depth_plot_295} #| include: true # plot <- ggplot() + geom_point(data = press_temp_295, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ### tag 304 This shark (female) stayed in the Oosterscheldt. ```{r depth_plot_304} #| include: true # plot <- ggplot() + geom_point(data = press_temp_304, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ### tag 310 This shark (male) stayed in the Oosterscheldt. ```{r depth_plot_310} #| include: true # plot <- ggplot() + geom_point(data = press_temp_310, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ### tag 312 This shark (female) stayed in the Oosterscheldt. ```{r depth_plot_312} #| include: true # plot <- ggplot() + geom_point(data = press_temp_312, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ### tag 319 This shark (female) swam out to the OWF. ```{r depth_plot_319} #| include: true # plot <- ggplot() + geom_point(data = press_temp_319, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ### tag 322 This shark (female) swam out to the OWF. ```{r depth_plot_322} #| include: true # plot <- ggplot() + geom_point(data = press_temp_322, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ::: ## depth and temp profiles of long term DST data These are the profiles of the 2 tags that recorded for over a year. ::: panel-tabset ### tag 308 This shark (female) swam south into the English Channel. ```{r depth_plot_308} #| include: true # plot <- ggplot() + geom_point(data = press_temp_308, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ### tag 321 This shark (male) swam north into the Southern North Sea. ```{r depth_plot_321} #| include: true # plot <- ggplot() + geom_point(data = press_temp_321, aes(x = date_time, y = -depth_m, color = temp_c)) + scale_y_continuous(breaks = c(0, -10, -20, -30, -40, -50, -60, -70)) + labs(y = "Depth in m", x = "Date", color = "Temperature in C") + scale_colour_distiller(palette ="RdYlBu") + theme_dark() # plotly::ggplotly(plot) ``` ::: ## what I plan to do now - I would like to segment the vertical data logs into behavioural patterns. This was done by Heerah et al., 2017 (<https://doi.org/10.1186/s40462-017-0111-3>). I spent a day with it already, but it is not trivial to format the code to the *M. asterias* DST data. To correctly do this, I would need the help of someone that has already done this. Jolien supervised a thesis in 2020 (Camille Boileau-Locas), that did that as well, but I know she has a lot to do right now. Mathieu Woillez also co-authored the paper by Karine Heerah, so I could contact him as well, potentially. What do you suggest? - The method of Heerah et al., 2017 uses STFT, a modified version of FFT. I was thinking that I maybe at first do a regular FFT to look at the periodic patterns of the signals. - Griffiths et al., 2020 (<https://doi.org/10.1371/journal.pone.0239480>) did not use any fancy/complex Hidden Markov Models like Heerah but they calculated statistics such as the time spent at certain depth bins, vertical speed, seabed proximity, or vertical movement behvaiour (based on a Wilcoxon test). - I am unsure right now what analyses could best shed light on behavioural patterns of the sharks. What are your suggestions?             :::